Download PDF
AccuWeather's Transformation: Predicting Business Impact of Weather with Azure Machine Learning
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Database Management & Storage
Applicable Industries
- Education
- Equipment & Machinery
Applicable Functions
- Logistics & Transportation
- Sales & Marketing
Use Cases
- Inventory Management
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
The Challenge
AccuWeather, a leading global provider of weather forecasts, was faced with the challenge of providing custom weather-impact predictions for business customers. Businesses wanted more than just weather data; they wanted predictions of how the weather would affect their operations. Initially, AccuWeather provided this service on an ad hoc consultative basis, which was labor-intensive. Customers would share their sales history data, and AccuWeather would have a data scientist clean the data and create a custom model. However, this process was not scalable. AccuWeather wanted to automate this service, allowing customers to upload their data online and generate an automated prediction. However, they were concerned that the analytical tools they had at the time wouldn’t allow them to provide high-quality predictions.
About The Customer
AccuWeather, Inc., based in State College, Pennsylvania, is a leading global provider of weather forecasts. It provides minute-by-minute forecasts for 2.3 million locations around the world for more than 100 parameters—including temperature, humidity, rain, snow, and ice—for every hour over the next 90 days and every minute for the next two hours. AccuWeather gathers hundreds of thousands of real-time weather observations from land, ships, aircraft, satellites, and radar and crunches that data using its patented AccuWeather Forecast Engine. More than 180,000 websites, 200 television stations, 900 radio stations, and 600 newspapers feature AccuWeather forecasts. In total, nearly two billion people worldwide rely on AccuWeather to help them plan their lives, protect their businesses, and stay safe.
The Solution
AccuWeather turned to Microsoft Azure to build an automated, scalable weather prediction service for business customers. They chose Azure for its solid infrastructure as a service offering, and its rich and rapidly advancing platform as a service portfolio, including big data, machine learning, and AI capabilities. AccuWeather first moved its API business to Azure, which provided a more scalable, cost-effective way to manage the service. They then moved their big data storage and processing, using services such as Azure Blob storage, Azure Data Factory, and Azure SQL Database. To create highly accurate forecasts, they used Azure's machine learning tools, which were powerful yet customizable using R and Python code. In May 2017, AccuWeather launched AccuWeather D3 Express, a cloud-based analytics product that quantifies the impact of disruptive weather on a business. They then developed D3 Advanced, which delivers even more customized predictions using machine learning.
Operational Impact
Quantitative Benefit
Related Case Studies.
Case Study
Smart Water Filtration Systems
Before working with Ayla Networks, Ozner was already using cloud connectivity to identify and solve water-filtration system malfunctions as well as to monitor filter cartridges for replacements.But, in June 2015, Ozner executives talked with Ayla about how the company might further improve its water systems with IoT technology. They liked what they heard from Ayla, but the executives needed to be sure that Ayla’s Agile IoT Platform provided the security and reliability Ozner required.
Case Study
IoT enabled Fleet Management with MindSphere
In view of growing competition, Gämmerler had a strong need to remain competitive via process optimization, reliability and gentle handling of printed products, even at highest press speeds. In addition, a digitalization initiative also included developing a key differentiation via data-driven services offers.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Premium Appliance Producer Innovates with Internet of Everything
Sub-Zero faced the largest product launch in the company’s history:It wanted to launch 60 new products as scheduled while simultaneously opening a new “greenfield” production facility, yet still adhering to stringent quality requirements and manage issues from new supply-chain partners. A the same time, it wanted to increase staff productivity time and collaboration while reducing travel and costs.
Case Study
Integration of PLC with IoT for Bosch Rexroth
The application arises from the need to monitor and anticipate the problems of one or more machines managed by a PLC. These problems, often resulting from the accumulation over time of small discrepancies, require, when they occur, ex post technical operations maintenance.
Case Study
Robot Saves Money and Time for US Custom Molding Company
Injection Technology (Itech) is a custom molder for a variety of clients that require precision plastic parts for such products as electric meter covers, dental appliance cases and spools. With 95 employees operating 23 molding machines in a 30,000 square foot plant, Itech wanted to reduce man hours and increase efficiency.
